{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import csv\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_path = \"./data/x_train_process.csv\"\n",
    "y_train_path = \"./data/y_train_process.csv\"\n",
    "x_test_path = \"./data/x_test_process.csv\"\n",
    "y_test_path = \"./data/y_test_process.csv\"\n",
    "\n",
    "def load_data(file_name):\n",
    "    bank_data = []\n",
    "    with open(file_name) as csvfile:\n",
    "        csv_reader = csv.reader(csvfile)\n",
    "        data_header = next(csv_reader)  \n",
    "#         print(data_header)\n",
    "        for row in csv_reader:  \n",
    "            bank_data.append(row)\n",
    "    return bank_data\n",
    "\n",
    "x_train = np.array(load_data(x_train_path)).astype(float)\n",
    "y_train = np.array(load_data(y_train_path)).astype(float)\n",
    "x_test = np.array(load_data(x_test_path)).astype(float)\n",
    "y_test = np.array(load_data(y_test_path)).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、 支持向量机"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/lib/python3.5/site-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集精确度:  0.971\n",
      "测试集精确度:  0.957\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC, NuSVC, LinearSVC\n",
    "model = SVC(kernel='rbf', degree=2, gamma=1.7)\n",
    "# 拟合训练数据集\n",
    "model.fit(x_train, y_train)\n",
    "# 预测训练集\n",
    "train_predict_y = model.predict(x_train)\n",
    "print(\"训练集精确度: \", accuracy_score(y_train, train_predict_y))\n",
    "# 预测测试集\n",
    "test_y_predict = model.predict(x_test)\n",
    "print(\"测试集精确度: \", accuracy_score(y_test, test_y_predict))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、 逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集精确度:  0.9666\n",
      "测试集精确度:  0.957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/lib/python3.5/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/ubuntu/lib/python3.5/site-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model.logistic import LogisticRegression\n",
    "LR = LogisticRegression()\n",
    "LR.fit(x_train, y_train)\n",
    "train_predict_y = LR.predict(x_train)\n",
    "print(\"训练集精确度: \", accuracy_score(y_train, train_predict_y))\n",
    "test_y_predict = LR.predict(x_test)\n",
    "print(\"测试集精确度: \", accuracy_score(y_test, test_y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、 神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:921: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集精确度:  0.9672\n",
      "测试集精确度:  0.9555\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "mlp = MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 5), random_state=1)\n",
    "mlp.fit(x_train, y_train)\n",
    "print(\"训练集精确度: \", mlp.score(x_train, y_train))\n",
    "print(\"测试集精确度: \", mlp.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、 决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集精确度:  1.0\n",
      "测试集精确度:  0.9145\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier(random_state=0)\n",
    "tree.fit(x_train, y_train)\n",
    "print(\"训练集精确度: \", tree.score(x_train, y_train))\n",
    "print(\"测试集精确度: \", tree.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 重要特征计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feat importance = [  9.45010544e-03   3.76201280e-03   0.00000000e+00   2.10934633e-03\n",
      "   7.03870127e-04   1.66426489e-02   4.71027286e-03   9.72949713e-04\n",
      "   4.82359579e-04   5.00000000e-04   1.13102150e-03   1.16262560e-03\n",
      "   6.57473085e-03   4.09237685e-05   3.01851676e-04   0.00000000e+00\n",
      "   0.00000000e+00   1.53772730e-03   7.04659521e-04   5.46318351e-04\n",
      "   1.32763119e-04   2.42493761e-04   1.81486912e-04   6.48964436e-05\n",
      "   6.25460294e-04   6.85003770e-05   4.57120097e-04   5.54779137e-05\n",
      "   1.16822656e-04   3.45313641e-04   1.58215224e-03   1.44028178e-03\n",
      "   1.24438130e-04   1.24071580e-04   0.00000000e+00   2.11282300e-04\n",
      "   2.66436782e-04   0.00000000e+00   5.14396867e-04   2.12395043e-04\n",
      "   2.68713586e-05   6.63636364e-04   5.01539727e-04   1.17127466e-04\n",
      "   2.15809845e-04   1.13381898e-03   4.90096878e-04   8.89450565e-04\n",
      "   1.74707936e-03   6.84235175e-04]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "feat_importance = tree.tree_.compute_feature_importances(normalize=False)\n",
    "keys = ['age', 'education', 'default', 'housing', 'loan', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate', 'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed', 'poutcome_failure', 'poutcome_nonexistent', 'poutcome_success', 'job_admin.', 'job_blue-collar', 'job_entrepreneur', 'job_housemaid', 'job_management', 'job_retired', 'job_self-employed', 'job_services', 'job_student', 'job_technician', 'job_unemployed', 'job_unknown', 'marital_divorced', 'marital_married', 'marital_single', 'marital_unknown', 'contact_cellular', 'contact_telephone', 'month_apr', 'month_aug', 'month_dec', 'month_jul', 'month_jun', 'month_mar', 'month_may', 'month_nov', 'month_oct', 'month_sep', 'day_of_week_fri', 'day_of_week_mon', 'day_of_week_thu', 'day_of_week_tue', 'day_of_week_wed']\n",
    "print(\"feat importance = \" + str(feat_importance))\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "\n",
    "#图像绘制\n",
    "fig,ax=plt.subplots(figsize=(20,10))\n",
    "b=ax.barh(keys,feat_importance,color='#6699CC')\n",
    "\n",
    "#设置Y轴刻度线标签\n",
    "ax.set_yticklabels(keys)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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